Measuring psychological stress from cardiovascular and activity signals

ABSTRACT

A method and system for measuring psychological stress disclosed. In a first aspect, the method comprises determining R-R intervals from an electrocardiogram (ECG) to calculate a standard deviation of the R-R intervals (SDNN) and determining a stress feature (SF) using the SDNN. In response to reaching a threshold, the method includes performing adaptation to update a probability mass function (PMF). The method includes determining a stress level (SL) using the SF and the updated PMF to continuously measure the psychological stress. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

CROSS-REFERENCE TO RELATED APPLICATION

Under 35 U.S.C. 120, this application is a Continuation Application andclaims priority to U.S. application Ser. No. 13/664,199, filed Oct. 30,2012, entitled “MEASURING PSYCHOLOGICAL STRESS FROM CARDIOVASCULAR ANDACTIVITY SIGNALS,” which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to sensor devices, and more particularly,to a sensor device utilized to measure psychological stress.

BACKGROUND

Psychological stress is associated with a variety of cardiovasculardiseases and quantitatively measuring stress aids in stress management.Conventionally, there are two types of psychological stress: acutestress and chronic stress. Acute stress is characterized by rapidchanges in the autonomic nervous system that ready the body for “fightor flight” responses to external stimuli. Chronic stress ischaracterized by prolonged exposure to stressful stimuli which leads tolong-term sympathetic overactivity.

Conventional methods of measuring stress calculate heart rate (HR) andheart rate variability (HRV) in the time and frequency domains. However,HR and HRV are highly variable between people. This variability issuelimits the continuous monitoring and accurate measuring of a person'spsychological stress levels. Therefore, there is a strong need for acost-effective solution that overcomes the above issue by adaptivelymeasuring individualized physiology. The present invention addressessuch a need.

SUMMARY OF THE INVENTION

A method and system for measuring psychological stress are disclosed. Ina first aspect, the method comprises determining R-R intervals from anelectrocardiogram (ECG) to calculate a standard deviation of the R-Rintervals (SDNN) and determining a stress feature (SF) using the SDNN.In response to reaching a threshold, the method includes performingadaptation to update a probability mass function (PMF). The methodincludes determining a stress level (SL) using the SF and the updatedPMF to continuously measure the psychological stress.

In a second aspect, the system comprises a wireless sensor devicecoupled to a user via at least one electrode, wherein the wirelesssensor device includes a processor and a memory device coupled to theprocessor, wherein the memory device stores an application which, whenexecuted by the processor, causes the processor to determine R-Rintervals from an electrocardiogram (ECG) to calculate a standarddeviation of the R-R intervals (SDNN) and determine a stress feature(SF) using the SDNN. In response to reaching a threshold, the systemfurther causes the processor to perform adaptation to update aprobability mass function (PMF). The system further causes the processorto determine a stress level (SL) using the SF and the updated PMF tocontinuously measure the psychological stress.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures illustrate several embodiments of the inventionand, together with the description, serve to explain the principles ofthe invention. One of ordinary skill in the art will recognize that theparticular embodiments illustrated in the figures are merely exemplary,and are not intended to limit the scope of the present invention.

FIG. 1 illustrates a wireless sensor device in accordance with anembodiment.

FIG. 2 illustrates a flow chart of a method for measuring psychologicalstress in accordance with an embodiment.

FIG. 3 illustrates a more detailed flow chart of a method for measuringpsychological stress in accordance with an embodiment.

FIG. 4 illustrates a more detailed flow chart of a method for adaptivefunction calibration in accordance with an embodiment.

FIG. 5 illustrates a diagram of stress level computation in accordancewith an embodiment.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention relates to sensor devices, and more particularly,to a sensor device utilized to measure psychological stress. Thefollowing description is presented to enable one of ordinary skill inthe art to make and use the invention and is provided in the context ofa patent application and its requirements. Various modifications to thepreferred embodiment and the generic principles and features describedherein will be readily apparent to those skilled in the art. Thus, thepresent invention is not intended to be limited to the embodiments shownbut is to be accorded the widest scope consistent with the principlesand features described herein.

When left untreated, acute and chronic stress leads to a variety ofhealth related challenges. Acute stress results in a “fight or flight”response to external stimuli. This response creates a short termincrease in sympathetic tone and a decrease in parasympathetic tone.Acute stress is also characterized by an increased HR, increased lowfrequency HRV, decreased high frequency HRV, and a decreased galvanicskin response (GSR). Chronic stress results in long-term sympatheticoveractivity. Chronic stress is also characterized by an increasedbaseline cortisol production, increased sympathetic activation,increased blood pressure, potentially decreased HRV, potential changesin HR, decreased physiological response to acute stress, and decreasedbaroreflex sensitivity.

HRV is related to the regulation of the heart by the autonomic nervoussystem. HRV is measured by a variety of time domain functions includingbut not limited to standard deviation of R-R intervals (SDNN),root-mean-square of successive R-R intervals (RMSSD), and a proportionof successive R-R intervals differing by a predetermined time period(e.g. >50 ms). An R-R interval is the interval from the peak of one QRScomplex to the peak of the next as shown on an electrocardiogram. HRV ismeasured by a variety of frequency domain functions including but notlimited to low frequency (LF) power from a predetermined range (e.g.0.04 to 0.15 Hz), high frequency (HF) power from a predetermined range(e.g. 0.15 to 0.4 Hz), and a LF/HF ratio.

A method and system in accordance with the present invention utilizes acombination of heart rate (HR) and heart rate variability determined viathe SDNN and specific posture analysis to continuously measure stresslevels of an individual person. Changing postures from sitting tostanding and then from standing to walking increases a person's HR anddecreases the HRV.

The person's psychological stress is measured by determining R-Rintervals from an electrocardiogram (ECG) to calculate a standarddeviation of the R-R intervals (SDNN) and instantaneous heart rate (HR),wherein a stress feature (SF) is determined using the SDNN and HR. Aftera predetermined time interval, adaptation is performed to update aprobability mass function (PMF) of the stress feature (SF) and a stresslevel (SL) is determined using the SF and the PMF to continuouslymeasure the psychological stress. The SL determination normalizes stressmeasurements between a value range of 0 to 1 because individual stressfeatures (SFs) are highly variable.

One of ordinary skill in the art readily recognizes that a variety ofsensor devices can be utilized for the measuring of psychological stressincluding portable wireless sensor devices with embedded circuitry in apatch form factor and that would be within the spirit and scope of thepresent invention.

To describe the features of the present invention in more detail, refernow to the following description in conjunction with the accompanyingFigures.

FIG. 1 illustrates a wireless sensor device 100 in accordance with anembodiment. The wireless sensor device 100 includes a sensor 102, aprocessor 104 coupled to the sensor 102, a memory 106 coupled to theprocessor 104, an application 108 coupled to the memory 106, and atransmitter 110 coupled to the application 108. The sensor 102 obtainsdata from the user and transmits the data to the memory 106 and in turnto the application 108. The processor 104 executes the application 108to process ECG signal information of the user. The information istransmitted to the transmitter 110 and in turn relayed to another useror device.

In one embodiment, the sensor 102 comprises two electrodes to measurecardiac activity and an accelerometer to record physical activity andposture and the processor 104 comprises a microprocessor. One ofordinary skill in the art readily recognizes that a variety of devicescan be utilized for the processor 104, the memory 106, the application108, and the transmitter 110 and that would be within the spirit andscope of the present invention.

FIG. 2 illustrates a flow chart of a method 200 for measuringpsychological stress in accordance with an embodiment. Referring toFIGS. 1 and 2 together, the method 200 comprises the wireless sensordevice 100 determining R-R intervals from an electrocardiogram (ECG) tocalculate a standard deviation of the R-R intervals (SDNN) and a heartrate (HR), via step 202, and determining a stress feature (SF) using theSDNN and the HR, via step 204. In response to reaching a threshold, themethod 200 includes performing adaptation to update a probability massfunction (PMF), via step 206. The method 200 includes determining astress level (SL) using the SF and the PMF to continuously measure thepsychological stress, via step 208.

In one embodiment, the method 200 further includes determining a posturestate, wherein the psychological stress is not measured if the posturestate is active. The posture state comprises a variety of statesincluding but not limited to active (e.g. walking, running, etc.),siting, and standing. A separate probability mass function (PMF) isstored for each possible posture. In another embodiment, the method 200further includes displaying the determined SL to a user or anotherdevice.

In one embodiment, determining R-R intervals from the ECG to calculatethe SDNN and the HR via step 202 comprises coupling the wireless sensordevice 100 via at least one electrode to measure the ECG of a user anddetecting R peaks from the ECG within a predetermined time period. Inthis embodiment, the R-R intervals are calculated using the detected Rpeaks.

In one embodiment, determining a stress feature (SF) using the SDNN andthe HR via step 204 comprises calculating a mean heart rate (HR) fromthe ECG within the predetermined time period and computing the SFutilizing an algorithm that includes the HR and the SDNN. In anembodiment, the algorithm is SF=HR+α*SDNN, wherein α is predeterminednegative variable that allows for combining HR and SDNN.

In another embodiment, if the wireless sensor device 100 records otherrelevant physiologic parameters, for example, galvanic skin response(GSR), skin temperature (TEMP), breathing rate (BR), and a square rootof the mean squared difference of successive NNs (RMSSD) is utilized tocompute HRV, the algorithm to compute SF is a linear combination of theparameters, for example, SF=α1*HR+α2*SDNN+α3*RMSSD+α4*GSR+α5*TEMP+α6*BR.In another embodiment, the SF is a non-linear combination of thesemeasures, for example, the algorithm isSF=HR²+α1*sqrt(SDNN)+α2*RMSSD*GSR orSF=α1*HR+α2*log(SDNN)+α3*TEMP²+α4*exp(−GSR).

In one embodiment, performing adaptation to update the PMF via step 206comprises grouping data into a predetermined distribution, calibratingthe predetermined distribution according to a detected resting heartrate, and adjusting the predetermined distribution according toadditional samples received. In an embodiment, adjusting thepredetermined distribution according to additional samples receivedcomprises multiplying all bins of the predetermined distribution by 1−εin response to data arriving and adding ε to a bin corresponding to thedata.

In one embodiment, determining the stress level (SL) using the SF andthe PMF via step 208 comprises adding all bins below a bin correspondingto the SF, and computing the SL utilizing an algorithm that includes aprobability mass function for a given posture (PMF_(posture)), the SF,and the added bins. In this embodiment, the method 200 further includesadding a fraction of a current bin of the SL to improve granularity.

Long-term (e.g. weeks, months) changes in the mean and standarddeviation of a stress feature probability mass function (PMF) reflectsincreases and/or decreases in stress. In one embodiment, the method 200further includes tracking both a mean and a standard deviation of aprobability mass function (PMF) as the PMF adapts over time andcombining the mean and standard deviation to measure long-term stress.

FIG. 3 illustrates a more detailed flow chart of a method 300 formeasuring psychological stress in accordance with an embodiment. Themethod 300 includes determining the current posture/activity of a user,via step 302. If the posture/activity is “active”, the method 300 doesnot compute a stress level. If the posture/activity is either “stand” or“sit”, the method 300 computes a stress level. The method 300 detectsR-peaks within a predetermined time period, via step 304, computes R-Rintervals, via step 306, computes a standard deviation of R-R intervals(SDNN), via step 308, and computes a heart rate (HR), via step 310.

Utilizing these computations, the method 300 computes a stress feature(SF) per the following equation: SF=HR+α*SDNN, via step 312. The SF ishighly variable between individuals and in one embodiment, is between apredetermined range of −20 to 160. Because of this variability, astandardized stress level with a value range between 0 and 1 is computedthat is relatively normalized between people. The α value is typicallynegative and is the weighting that allows for combining HR and SDNN andin one embodiment, a is defaulted as −0.315. The method 300 includesfinding a correct bin for the SF per the following equation: IF(SF>=binedge_(posture)[i] AND SF<binedge_(posture)[i+1]), THEN B=i, viastep 314.

The binedge_(posture)[i] includes the edges of the bins for the stressfeature and the number of bins and spacing of bin edges is set dependingupon desired granularity. In one embodiment, for 180 bins from −20 to160, bin edges are set as −20, −19, −18 . . . , 159, 160). The bins areused for the PMF/histogram and B is the bin that the current SF fallsinto.

The method 300 determines whether more than a predetermined number ofseconds (T) has passed since a last adaptation, via step 316. In oneembodiment, T is defaulted as 600 seconds. If yes (more than T secondshave passed since the last adaptation), then the method 300 updates aprobability mass function (PMF) via an adaptation function per thefollowing equation: PMF_(posture)=PMF_(posture)*(1−ε) andPMF_(posture)[B]=PMF_(posture)[B]+ε, where ε is a “forgetting” parameterfor how much the PMF/histogram is changed with each adaptation run, viastep 318. In one embodiment, ε is defaulted as 0.0003. The method 300retrieves a probability mass function (PMF) for the given posture asPMF_(posture), via step 320, which is used to calculate the stress level(SL).

The stress level (SL) measures the stress of an individual on a scalefrom 0 to 1, where 0 indicates no or very little stress and 1 indicatesextremely high stress. The method 300 computes the SL per the followingequation, via step 322:

${S\; L} = {{\sum\limits_{i = 1}^{B}{{PMF}_{posture}\lbrack i\rbrack}} + {\frac{{SF} - {{binedge}\lbrack B\rbrack}}{{{binedge}\left\lbrack {B + 1} \right\rbrack} - {{binedge}\lbrack B\rbrack}}.}}$In this equation, all the bins below the current bin B that the SF fallsinto are added and a fraction of the current bin B is added to result inimproved granularity. There is a separate probability mass function(PMF) for each posture because different postures have different HR andSDNN values. The method 300 determines whether the computed SL isgreater than a threshold (th) for more than N minutes, via step 324. Ifyes, an alert is presented to the user. After the SL is computed, it isdisplayed via the wireless sensor device 100.

In one embodiment, the adaptive function comprises initializing,calibrating, and adapting steps. The initializing step includesbeginning with a group probability mass function (PMF) that is adiscretized Gaussian distribution predetermined from group trainingdata. The calibrating step includes shifting the probabilitydistribution according to detected resting heart rates. The adaptingstep includes adjusting the PMF as new samples arrive or asfrequently/infrequently as desired. When new data arrives, all bins aremultiplied by 1−ε(e.g. 0.9997) and ε (e.g. 0.0003) is added to a bincorresponding to the new data. This adaptation adjusts the probabilitydistribution over the course of days to weeks to fit the particularperson's average distribution of stress over the course of the day.

FIG. 4 illustrates a more detailed flow chart of a method 400 foradaptive function calibration in accordance with an embodiment. Themethod 400 includes initializing a stress feature probability densityfunction (PDF) to a predetermined group model per the followingequation: PDF_(SF)˜N(μ_(SFgroup), σ² _(SFgroup)), via step 402. Thenotation N(μ,σ²) is a normal/Gaussian distribution with mean μ andvariance σ² and μ_(SFgroup) and σ² _(SFgroup) are predetermined from thegroup training data.

The method 400 determines a resting heart rate (HR_(rest)) of a userduring a period of low or no activity, via step 404. The HR_(rest) isestimated from the user's data and can be during no activity or sleepperiods. The method 400 estimates an individual heart rate (HR) PDF perthe following equation: PDF_(HR)˜N(HR_(rest+)γ*σ_(HRindiv), σ²_(HRindiv)), via step 406 and estimates an individual SDNN PDF per thefollowing equation: PDF_(SDNN)˜N(μ_(SDNNindiv), σ² _(SDNNindiv)), viastep 408. The method 400 computes a new stress feature PDF by combiningthe determined HR and SDNN PDFs per the following equation:PDF_(SF)˜N(HR_(rest)+γ*σ_(HRindiv)+α*μ_(SDNNindiv),σ² _(HRindiv)+α²*σ²_(SDNNindiv)), via step 410. The continuous PDF_(SF) is converted to adiscretized probability distribution, or probability mass function(PMF_(SF)), via step 412. In one embodiment, the conversion is done bysampling the PDF_(SF) within a predetermined interval and normalizingthe sum to 1.

In one embodiment, the γ value is a constant offset above HR_(rest) forthe mean of the HR distribution (e.g. γ=2). The σ_(HRindiv),σ_(SDNNindiv), μ_(SDNNindiv) values are predetermined and fixed and arecomputed from the group training data. The σ_(HRindiv) and σ_(SDNNindiv)values are computed as the mean of the individual standard deviations inthe group training data and the μ_(SDNNindiv) value is computed as themean of the mean SDNN of all individuals in the group training data.

Long-term changes in the mean and standard deviation of the stressfeature PMF reflect increases or decreases in stress. Increased chronicstress is seen in an upwards long-term shift of the SF probabilitydistribution and increased chronic stress also decreases the response toacute stress, resulting in a narrow SF probability distribution or asmaller standard deviation.

In one embodiment, tracking the mean and the standard deviation of thisSF probability distribution as it adapts over time and combining theseresults enables a measure of long-term stress per the followingequation: SF_(long)=μ_(SF)−β*σ_(SF), where μ_(SF) is the mean of the SFfrom the current probability distribution

$\left( {\mu_{SF} = {\sum\limits_{i}{{SF}_{i}*{{PMF}\left\lbrack {SF}_{i} \right\rbrack}}}} \right),$σ_(SF) is the standard deviation of the SF from the current probabilitydistribution

$\left( {\sigma_{SF}^{2} = {\sum\limits_{i}{\left( {{SF}_{i} - \mu_{SF}} \right)^{2}*{{PMF}\left\lbrack {SF}_{i} \right\rbrack}}}} \right),$and B is a positive value.

If a person's mean stress feature probability mass function/probabilitydistribution (PMF_(SF)) increases over time, μ_(SF) will increase andSF_(long) will increase. If a person becomes less responsive to acutestress due to chronic stress, the standard deviation of the SFdistribution will decrease because they are not responding to differentincreases/decreases in acute stress, resulting in the σ_(SF) decreasingand the SF_(long) once again increasing. In one embodiment, SF_(long) iscomputed from the current PMF_(SF) at various time periods including butnot limited to once every few days and tracked over various time periodsincluding but not limited to weeks and months.

In one embodiment, to determine the group training data, users aresubjected to alternating blocks of relaxation and stress. The blocksranged from 3 to 7 minutes in length, relaxation involved various actsincluding but not limited to sitting quietly or listening to classicalmusic, and stress involved various acts including but not limited towatching a movie clip from an active/horror movie, playing tetris,performing a stroop test, performing a series of mental arithmeticproblems, and playing a competitive online real-time strategy game.

FIG. 5 illustrates a diagram 500 of stress level computation inaccordance with an embodiment. In the diagram 500, the y axis representsthe stress level from 0 to 1 and the x axis represents time. Apredetermined window of time for computing the stress level is variabledepending on the necessary time resolution and the application (e.g.gaming versus daily use). Shorter windows allow changes in stress to bedetected much faster but include additional noise. During periods ofstress 502, such as playing a game, the stress level increases to valuescloser to 1.

Additionally, while the probability mass function (PMF) is adapted foreach person, the best stress feature or the best combination of HR andSDNN is learnable for each person. In one embodiment, individuallearning is done via supervised learning including but not limited toFisher Discriminants that learn the best α, which is the weightingparameter for combining HR and SDNN for each person.

In an embodiment, a semi-supervised approached is utilized to learn thebest feature including but not limited to self-training where anindividual performs a few minutes of a relaxation activity (e.g.metronome breathing) and a few minutes of a stressful activity (e.g.playing tetris). The two data points are used to determine an initialprojection line defined by the α parameter and new data is classifiedand the most confident data points are used by the wireless sensordevice 100 to continuously and automatically adjust the α parameter.

As above described, the method and system allow for measuringpsychological stress of a user of a wireless sensor device. Bydetermining current posture, detecting R-peaks from an ECG within apredetermined window of time to compute mean heart rate (HR) and SDNN,combining the HR and the SDNN to calculate a stress feature (SF) that ishighly variable between different people, determining a current bin thatthe SF falls into within a predetermined bin range, and determining alatest probability mass function (PMF) and summing all bins of the PMFbelow the current bin, a cost-effective and continuous stress level (SL)measurement system is achieved.

The predetermined window of time includes but is not limited to 120seconds and the predetermined bin range includes but is not limited to−20 to 160 with a width of 1. If a threshold time period has passedsince last adaptation, the method and system perform adaptation of theprobability mass function (PMF)/probability distribution using thecurrent SF. The current stress level (SL) of the user is eitherdisplayed to the user via the wireless sensor device and/or triggers awarning alert if the SL is above a threshold (th) longer than apredetermined time period of N minutes.

A method and system for measuring psychological stress has beendisclosed. Embodiments described herein can take the form of an entirelyhardware implementation, an entirely software implementation, or animplementation containing both hardware and software elements.Embodiments may be implemented in software, which includes, but is notlimited to, application software, firmware, resident software,microcode, etc.

The steps described herein may be implemented using any suitablecontroller or processor, and software application, which may be storedon any suitable storage location or computer-readable medium. Thesoftware application provides instructions that enable the processor tocause the receiver to perform the functions described herein.

Furthermore, embodiments may take the form of a computer program productaccessible from a computer-usable or computer-readable storage mediumproviding program code or program instructions for use by or inconnection with a computer or any instruction execution system. For thepurposes of this description, a computer-usable or computer-readablestorage medium can be any apparatus that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.

The computer-readable storage medium may be an electronic, magnetic,optical, electromagnetic, infrared, semiconductor system (or apparatusor device), or a propagation medium. Examples of a computer-readablestorage medium include a semiconductor or solid state memory, magnetictape, a removable computer diskette, a random access memory (RAM), aread-only memory (ROM), a rigid magnetic disk, and an optical disk.Current examples of optical disks include DVD, compact disk-read-onlymemory (CD-ROM), and compact disk-read/write (CD-R/W).

Although the present invention has been described in accordance with theembodiments shown, one of ordinary skill in the art will readilyrecognize that there could be variations to the embodiments and thosevariations would be within the spirit and scope of the presentinvention. Accordingly, many modifications may be made by one ofordinary skill in the art without departing from the spirit and scope ofthe appended claims.

What is claimed is:
 1. A method for measuring psychological stress, themethod comprising: determining R-R intervals from an electrocardiogram(ECG) to calculate a standard deviation of the R-R intervals (SDNN),wherein the determining the R-R intervals from the ECG to calculate theSDNN further comprises: detecting R peaks from a measured ECG within apredetermined time period, and calculating the R-R intervals using thedetected R peaks; determining a stress feature (SF) using the SDNN,wherein the determining the stress feature (SF) using the SDNN furthercomprises: calculating a mean heart rate (HR) from the ECG within thepredetermined time period, and calculating the SF utilizing an algorithmSF=HR+α*SDNN, wherein the α includes a predetermined negative variablethat allows for combining the HR and the SDNN; in response to reaching athreshold, performing adaptation to update a probability mass function(PMF); and determining a stress level (SL) using the SF and the updatedPMF to continuously measure the psychological stress.
 2. The method ofclaim 1, further comprising: determining a posture state, wherein thepsychological stress is not measured if the posture state is active; anddisplaying the determined SL to a user or another device.
 3. The methodof claim 2, wherein the posture state includes any of active, sitting,and standing.
 4. The method of claim 1, wherein performing adaptation toupdate the PMF further comprises: grouping data into a predetermineddistribution; calibrating the predetermined distribution according to adetected resting heart rate; and adjusting the predetermineddistribution according to additional samples received.
 5. The method ofclaim 4, wherein adjusting the predetermined distribution according toadditional samples received further comprises: in response to dataarriving, multiplying all bins of the predetermined distribution by 1−ε;and adding ε to a bin corresponding to the data, wherein the ε includesa forgetting parameter for how much the PMF changes with each adaptationrun.
 6. The method of claim 4, wherein determining the stress level (SL)using the SF and the PMF further comprises: adding all bins below a bincorresponding to the SF; and computing the SL utilizing an algorithmthat includes a probability mass function for a given posture(PMF_(posture)), the SF, and the added bins.
 7. The method of claim 6,further comprising: adding a fraction of a current bin to improvegranularity.
 8. The method of claim 1, further comprising: tracking botha mean and a standard deviation of a probability distribution as theprobability distribution adapts over time; and combining the mean andthe standard deviation to measure long-term stress.
 9. The method ofclaim 1, wherein the application further causes the processor to: trackboth a mean and a standard deviation of a probability distribution asthe probability distribution adapts over time; and combine the mean andthe standard deviation to measure long-term stress.
 10. A system formeasuring psychological stress, the system comprising: a wireless sensordevice coupled to a user via at least one electrode, wherein thewireless sensor device includes a processor; and a memory device coupledto the processor, wherein the memory device stores an application which,when executed by the processor, causes the processor to: determine R-Rintervals from an electrocardiogram (ECG) to calculate a standarddeviation of the R-R intervals (SDNN), wherein the to determine the R-Rintervals from the ECG to calculate the SDNN further comprises: detect Rpeaks from a measured ECG within a predetermined time period, andcalculate the R-R intervals using the detected R peaks; determine astress feature (SF) using the SDNN, wherein the to determine the stressfeature (SF) using the SDNN further comprises: calculate a mean heartrate (HR) from the ECG within the predetermined time period, andcalculate the SF utilizing an algorithm SF=HR+α*SDNN, wherein the αincludes a predetermined negative variable that allows for combining theHR and the SDNN; in response to reaching a threshold, perform adaptationto update a probability mass function (PMF); and determine a stresslevel (SL) using the SF and the updated PMF to continuously measure thepsychological stress.
 11. The system of claim 10, wherein theapplication further causes the processor to: determine a posture state,wherein the psychological stress is not measured if the posture state isactive; and display the determined SL to a user or another device. 12.The system of claim 11, wherein the posture state includes any ofactive, sitting, and standing.
 13. The system of claim 10, wherein toperform adaptation to update the PMF further comprises to: group datainto a predetermined distribution; calibrate the predetermineddistribution according to a detected resting heart rate; and adjust thepredetermined distribution according to additional samples received. 14.The system of claim 13, wherein to adjust the predetermined distributionaccording to additional samples received further comprises to: inresponse to data arriving, multiply all bins of the predetermineddistribution by 1−ε; and add ε to a bin corresponding to the data,wherein the ε includes a forgetting parameter for how much the PMFchanges with each adaptation run.
 15. The system of claim 13, wherein todetermine the stress level (SL) using the SF and the PMF furthercomprises to: add all bins below a bin corresponding to the SF; andcompute the SL utilizing an algorithm that includes a probability massfunction for a given posture (PMF_(posture)), the SF, and the addedbins.
 16. The system of claim 15, wherein the application further causesthe processor to: add a fraction of a current bin to improvegranularity.